Artificial Intelligence, in the form of Machine Learning (ML), has already transformed medicine, retail sales, and other industries. It is about to enter all our lives, whether we want it or not! In this session, we will quickly develop a basic understanding of AI, and its advantages, applications, and difficulties.

1. Meet the Learning Machine
J Scott Christianson
Associate Teaching Professor
How Artificial Intelligence is transforming our world!

2. Artificial Intelligence (AI)
Definitions
Artificial Intelligence: A machine that exhibits
cognitive or decision-making behavior and
can take action to achieve a goal.

3. • General AI: A machine that can
reason and adapt like a human. E.g,
sci fi movies.
Artificial Intelligence (AI)
Definitions
Artificial Intelligence: A machine that exhibits
cognitive or decision-making behavior and
can take action to achieve a goal.

4. • General AI: A machine that can
reason and adapt like a human. E.g,
sci fi movies.
Artificial Intelligence (AI)
Definitions
• : A machine that is
optimized for a particular task or
project.
Narrow AI
Artificial Intelligence: A machine that exhibits
cognitive or decision-making behavior and
can take action to achieve a goal.

5. Machine Learning and Deep Learning
Narrow AI
Machine Learning
Deep Learning
Machine Learning’s goal is to
develop predictions based on
previously observed patterns.
Various variables are weighed to
predict the probabilities of the
outcomes. The variables and
formula used to make such
predictions may be programmed
by a human OR
they can be developed by the
machine itself (Deep Learning).

6. Deep Learning
01
Collect
Training
Data
02
Analyze
and
Segment
03
Setup and
Train a
Neural
Network
04
Test and
deploy

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10. Types of Data For ML Processing
• Motion Data
• Audio/Voice Data
• Image Data
• Text Data
• Geospatial Data
• Physiological/Medical
Data
• Financial Data
• Behavioral Data
• and Much More

11. Types of Data For ML Processing
• Motion Data
• Audio/Voice Data
• Image Data
• Text Data
• Geospatial Data
• Physiological/Medical
Data
• Financial Data
• Behavioral Data
• and Much More

14. Figure 2: Applications of AI algorithms in medicine. The left panel shows the image fed into an
algorithm. The right panel shows a region of potentially dangerous cells, as identified by an
algorithm, that a physician should look at more closely. (From Artificial Intelligence in Medicine:
Applications, implications, and limitations by Daniel Greenfield.)

15. More than 50 AI/ML algorithms have been cleared by the
US Food and Drug Administration for uses that include
identifying intracranial hemorrhage from brain computed
tomographic scans and detecting seizures in real time.
Algorithms are also used to inform clinical operations, such
as predicting which patients will “no show” for scheduled
appointments. More recently, algorithms that predict in-
hospital mortality have been proposed to inform ventilator
allocation during the coronavirus disease 2019 pandemic.
JAMA Article by Stephanie Eaneff, MSP1,2; Ziad Obermeyer, MD3; Atul J. Butte,
MD, PhD2,4

16. Types of Data For ML Processing
• Motion Data
• Audio/Voice Data
• Image Data
• Text Data
• Geospatial Data
• Physiological/Medical
Data
• Financial Data
• Behavioral Data
• and Much More

17. Interactive Voice Response
IF..Then Based Systems

18. Interactive Voice Response
ML Based Systems

19. When and How to Use ML
• Autonomous Vehicles
AI and Ethics

20. When and How to Use ML
• Autonomous Vehicles
• Admissions and Grading
AI and Ethics

21. When and How to Use ML
• Autonomous Vehicles
• Admissions and Grading
• Loans and Credit
AI and Ethics

22. When and How to Use ML
• Autonomous Vehicles
• Admissions and Grading
• Loans and Credit
• Social Media
AI and Ethics

23. When and How to Use ML
• Autonomous Vehicles
• Admissions and Grading
• Loans and Credit
• Social Media
• Warfare
AI and Ethics

24. Problems with AI

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Problems with AI
“Hidden Layers”

26. Problems with AI
Adversarial AI
from Savan Visalpara

27. Problems with AI
Adversarial AI
from Weijia Zhang

28. Problems with AI
Adversarial AI
from MIT CSAIL

29. Problems with AI
Adversarial AI
Images: Evtimov et al
Camouflage graffiti and art stickers cause a neural network to
misclassify stop signs as speed limit 45 signs or yield signs.

30. http://LearnAbout.AI